On Mitigating DIS Attacks in IoT Networks
Bibliographic record
Abstract
Routing protocols deem a pivotal component of the communication stack in the Internet of Things (IoT). The ipv6 Routing Protocol for Low power and lossy networks (RPL) has been standardized by the Internet Engineering Task Force (IETF) for routing in IoT-based networks. RPL-related control messages are transmitted in the network to construct an optimized forwarding structure. A malicious insider node can attack RPL networks by sending a high number of unnecessary control messages which causes a detrimental side effect on the network performance. One of these attacks targets DIS control messages transmitted by a new node to join the network. This attack is called the DIS attack. The attacker can exploit the joining process to flood the network with a large volume of DIS messages. This paper aims to investigate the effect of DIS attacks on network performance and develop an effective technique to mitigate the adverse effects of such attacks. The proposed technique is implemented in the Contiki operating system and evaluated using the Cooja emulator. Compared to the standard RPL and other comparable work in the literature, the proposed technique retains low routing control cost, high throughput, and low energy consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".